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executable file
·109 lines (99 loc) · 3.09 KB
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#!/usr/bin/env python
#
# cdict: dictionary representing a particular clustering output
# ldict: dictionary representing the ground-truth or gold-standard data
#
# Format for both dictionaries:
# item -> set of assigned clusters/real categories
#
# The values are sets to support overlapping clustering and ground-truth categories.
#
# Examples and extended BCubed computation taken from:
# Amigo, Enrique, et al. "A comparison of extrinsic clustering evaluation
# metrics based on formal constraints."
# Information retrieval 12.4 (2009): 461-486.
"""Examples for computing extended BCubed."""
import bcubed
def compute(title, cdict, ldict):
"""Compute extended BCubed precision and recall, and print the results."""
precision = bcubed.precision(cdict, ldict)
recall = bcubed.recall(cdict, ldict)
fscore = bcubed.fscore(precision, recall)
print "{}: precision={:.2f}, recall={:.2f}, fscore={:.2f}".format(
title, precision, recall, fscore)
# example ground-truth data (ldict)
ground_truth = {
"item1": set(["gray", "black"]),
"item2": set(["gray", "black"]),
"item3": set(["gray"]),
"item4": set(["black"]),
"item5": set(["black"]),
"item6": set(["dashed"]),
"item7": set(["dashed"]),
}
# example clustering (cdict) in page 24, figure 16
clustering = {
"item1": set(["A", "B"]),
"item2": set(["A", "B"]),
"item3": set(["A"]),
"item4": set(["B"]),
"item5": set(["B"]),
"item6": set(["C"]),
"item7": set(["C"]),
}
compute("page 24, figure 16", clustering, ground_truth)
# example clustering (cdict) in page 24, figure 17
clustering = {
"item1": set(["ADup", "A", "B"]),
"item2": set(["ADup", "A", "B"]),
"item3": set(["ADup", "A"]),
"item4": set(["B"]),
"item5": set(["B"]),
"item6": set(["C"]),
"item7": set(["C"]),
}
compute("page 24, figure 17", clustering, ground_truth)
# example clustering (cdict) in page 24, figure 18
clustering = {
"item1": set(["ADup2", "ADup", "A", "B"]),
"item2": set(["ADup2", "ADup", "A", "B"]),
"item3": set(["ADup2", "ADup", "A"]),
"item4": set(["B"]),
"item5": set(["B"]),
"item6": set(["C"]),
"item7": set(["C"]),
}
compute("page 24, figure 18", clustering, ground_truth)
# example clustering (cdict) in page 24, figure 19
clustering = {
"item1": set(["A"]),
"item2": set(["A"]),
"item3": set(["A"]),
"item4": set(["B"]),
"item5": set(["B"]),
"item6": set(["C"]),
"item7": set(["C"]),
}
compute("page 24, figure 19", clustering, ground_truth)
# example clustering (cdict) in page 25, figure 20
clustering = {
"item1": set(["A", "B"]),
"item2": set(["A", "B"]),
"item3": set(["A"]),
"item4": set(["D"]),
"item5": set(["D"]),
"item6": set(["C"]),
"item7": set(["C"]),
}
compute("page 25, figure 20", clustering, ground_truth)
# example clustering (cdict) in page 25, figure 21
clustering = {
"item1": set(["A"]),
"item2": set(["A"]),
"item3": set(["A"]),
"item4": set(["A"]),
"item5": set(["A"]),
"item6": set(["B"]),
"item7": set(["B"]),
}
compute("page 25, figure 21", clustering, ground_truth)